
#setting up our workign enviornment
!pip install yfinance
!pip install pandas
!pip install matplotlib.pyplot


import yfinance as yf
import pandas as pd
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import numpy as np
import requests
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from bs4 import BeautifulSoup
import string

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#Defined the start date and end date of the data.
startDate = '2018-01-01'
endDate = '2022-12-31'

#Defines the list of Dow Jones Industrial Average (DJIA) tickers.
names = ['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS', 'DOW', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']

#Gets the data for the DJIA stocks using the start and end dates we selected
data = {t: yf.download(t, start=startDate, end=endDate) for t in names}

#Combines the data in DJIA into a single dataset.
data2 = yf.download(names, start=startDate, end=endDate, group_by='t')

#Removes the data from 2019 and 2021 using != operator
data2 = data2[data2.index.year != 2019]
data2 = data2[data2.index.year != 2021]

#Uses a for loop to calculate the high/low average for every company in DJIA.
for company in data2.columns.levels[0]:
    data2[company, 'Average'] = (data2[company, 'High'] + data2[company, 'Low']) / 2

#Sorts the columns alphabetically, sets axis=1 to perform a column-wise operation..
data2 = data2.sort_index(axis=1)

#Prints the DataFrame.
data2

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#Since the data originally started in 1962 we wanted to isolate a certain section of time to analyze in order to look at the impact of covid
#Here we are isolating 2018 specifically to look at a pre covid year, we are setting the start date as January 1st 2018 and the end date as December 31 2018

startOne = '2018-01-01'
endOne = '2018-12-31'

#Now we download the yahoo finance library in order to extract the data
#We use startOne and endOne in order to create a table for 2018 data

start_data = yf.download('^DJI', start=startOne, end=endOne)

#Here we are isolating 2020 specifically to look at a year in the middle of peak covid, we are setting the start date as January 1st 2020 and the end date as December 31 2020

startTwo = '2020-01-01'
endTwo = '2020-12-31'

#We use startTwo and endTwo in order to create a table for 2020 data

mid_data = yf.download('^DJI', start=startTwo, end=endTwo)

#Here we are isolating 2022 specifically to look at a post covid year, we are setting the start date as January 1st 2022 and the end date as December 31 2022

startThree = '2022-01-01'
endThree = '2022-12-31'

#We use startThree and endThree in order to create a table for 2022 data

end_data = yf.download('^DJI', start=startThree, end=endThree)

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# Displaying the Dow Jones market prices every working day from the year 2018

start_data


# Displaying the Dow Jones market prices every working day from the year 2020

mid_data


# Displaying the Dow Jones market prices every working day from the year 2022.

end_data


# We now are displaying the highs, lows, and average stock price for each company in the Dow Jones Industrial per day.


highs = data2.loc[:, (slice(None), 'High')]
lows = data2.loc[:, (slice(None), 'Low')]
averages = data2.loc[:, (slice(None), 'Average')]
result = pd.concat([highs, lows, averages], axis = 1)
result


fig, axes = plt.subplots(nrows = 3, ncols = 1, figsize = (12, 12))
# Plot pre-COVID data
axes[0].plot(start_data['Close'], color = 'blue')
axes[0].set_title('Pre-Covid')

# Plot mid-COVID data
axes[1].plot(mid_data['Close'], color = 'green')
axes[1].set_title('Peak Covid')

# Plot current data
axes[2].plot(end_data['Close'], color = 'red')
axes[2].set_title('Post Covid')


fig.text(0.5, 0.04, 'Date', ha = 'center')
fig.text(0.04, 0.5, 'DJIA', va = 'center', rotation = 'vertical')

plt.tight_layout()
plt.show()


plt.figure(figsize = (18,10))

plt.plot(start_data["Close"], label = 'Pre-Covid', color='blue')

# Plot the second DJIA dataset in green color
plt.plot(mid_data["Close"], label = 'Peak-Covid', color='green')

# Plot the third DJIA dataset in red color
plt.plot(end_data["Close"], label = 'Post-Covid', color='red')


plt.xlabel('Date')
plt.ylabel('DJIA')
plt.title('DJIA Comparison')
plt.legend()
plt.show()


#Here we are looking at the data from yahoo finance, grabbing data from djia, sp500, and nasdaq between 2018 and 2022
djia = yf.download('^DJI', start = startDate, end = endDate)
sp500 = yf.download('^GSPC', start = startDate, end = endDate)
nasdaq = yf.download('^IXIC', start = startDate, end = endDate)

#We are looking at the close columns for each stock in order to retrieve the closing prices
#We select the column which contains the adjusted closing prices of the Dow Jones, S&P 500, and NASDAQ indices from the djia dataframe
#Then we are grouping the data by year by using the '.resample()' method and return the values necessary with the '.last()' function
#Then we are simply finding the percent change of each index using '.pct_change()'
djiaNew = djia['Adj Close'].resample('Y').last().pct_change()
sp500New = sp500['Adj Close'].resample('Y').last().pct_change()
nasdaqNew = nasdaq['Adj Close'].resample('Y').last().pct_change()

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# Here we are creating a bar chart to show the changes of each index in the given time period
#Storing data in data3 with x axis of the indices and y axis of the percent change of them
data3 = [go.Bar(x = ['DJIA', 'S&P 500', 'NASDAQ'], y = [djiaNew[-1], sp500New[-1], nasdaqNew[-1]])]

#here we are setting the title of the graph to: 'Index Returns from 2018-2022' and the title of the y-axis to: 'Returns'
lay = go.Layout(title = 'Index Returns from 2018-2022', yaxis = dict(title = 'Returns'))

#here we are creating a plot using data3
blueBar = go.Figure(data=data3, layout=lay)
blueBar.show()


#Choosing all closing prices of each year from columns named 'close'
close = data2.xs('Close', level=1, axis=1)

# Grouping by average closing price per year for each company
closeMean = close.groupby(close.index.year).mean()

# Creating a color map with unique colors
colors = plt.cm.viridis(np.linspace(0, 1, len(close.columns)))

# Plotting the average closing price for each company in the form of a bar chart
closeMean.plot(kind='bar', figsize=(25, 10), color=colors)


# Adding necessary labels for plot.
plt.xlabel('Year')
plt.ylabel('Closing Price')
plt.title('DJIA Closing Prices (2018-2022)')
plt.show()

## DOW Inc became a company in 2019, thats why the 2018 section does not have a bar.


#Pie chart showing sector comp in djia


# Naming sectors and pairing them with percentages that they hold of the Dow Jones
sectors = ['Information Technology', 'Health Care', 'Consumer Discretionary', 'Communication Services', 'Industrials', 'Financials', 'Consumer Staples', 'Utilities', 'Materials', 'Real Estate', 'Energy']
percentages = [27.17, 15.05, 12.46, 10.15, 7.58, 7.06, 6.02, 3.28, 2.52, 2.35, 1.39]


# Plotting a pie chart
plt.pie(percentages, labels=sectors,autopct='%1.1f%%')
# Adding a relevant title
plt.title('Dow Jones Industrial Average Sectors')
plt.show()

## As of August 31, 2020: three companies(Pfizer, Raytheon, Exxon) were replaced for companies (Salesforce.com, Amgen, and Honeywell International)


#Here we are importing a url and setting it equal to url2018
url2018 = "https://www.cnbc.com/2018/12/20/us-futures-following-wednesdays-sell-off.html"

#here we are retrieving the content of the url2018
request = requests.get(url2018)

#we are using BeautifulSoup to retrieve the text from html
beau = BeautifulSoup(request.content, 'html.parser')
text = beau.get_text()

#setting the text to lowercase
text = text.lower()
#removing all the punctuation
text = text.translate(str.maketrans('', '', string.punctuation)) 
#separating the text into individual words
text = nltk.word_tokenize(text)
#making stopwords
stopWords = set(stopwords.words("english"))

#here we are removing words that did not appear in the article but showed up in the data frequency distribution 
removeWords = set(["accountsbest", "credit", "personal", "small", "loans"])
#creating a list of words while filtering out certain words such as the removeWords and stopWords 
text = list(word for word in text if word.isalpha() and len(word) > 2 and word.casefold() not in stopWords and word.casefold() not in removeWords)
#create a frequency distribution using text
frequencyPlot = FreqDist(text)

#creating a title for the plot, and setting it to font size 14
plt.title('Frequency Distribution 2018', fontsize = 14) 

#plotting 25 words
frequencyPlot.plot(25)

<Axes: title={'center': 'Frequency Distribution 2018'}, xlabel='Samples', ylabel='Counts'>


import requests
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from bs4 import BeautifulSoup
import string
import matplotlib.pyplot as plt

#Here we are importing a url and setting it equal to url2020
url2020 = "https://www.cnbc.com/2020/03/15/traders-await-futures-open-after-fed-cuts-rates-launches-easing-program.html"

#here we are retrieving the content of the url2020
request = requests.get(url2020)

#we are using BeautifulSoup to retrieve the text from html
beau = BeautifulSoup(request.content, 'html.parser')
text = beau.get_text()

#setting the text to lowercase
text = text.lower()
#removing all the punctuation
text = text.translate(str.maketrans('', '', string.punctuation)) 
#separating the text into individual words
text = nltk.word_tokenize(text)
#making stopwords
stopWords = set(stopwords.words("english"))

#here we are removing words that did not appear in the article but showed up in the data frequency distribution 
removeWords = set(["accountsbest", "credit", "personal", "small", "loans"])
#creating a list of words while filtering out certain words such as the removeWords and stopWords 
text = list(word for word in text if word.isalpha() and len(word) > 2 and word.casefold() not in stopWords and word.casefold() not in removeWords)
#create a frequency distribution using text
frequencyPlot = FreqDist(text)

#creating a title for the plot, and setting it to font size 14
plt.title('Frequency Distribution 2020', fontsize = 14) 

#plotting 25 words
frequencyPlot.plot(25)

<Axes: title={'center': 'Frequency Distribution 2020'}, xlabel='Samples', ylabel='Counts'>


#Here we are importing a url and setting it equal to url2022
url2022 = "https://www.cnbc.com/2022/12/27/stock-market-futures-open-to-close-news.html"

#here we are retrieving the content of the url2022
request = requests.get(url2022)

#we are using BeautifulSoup to retrieve the text from html
beau = BeautifulSoup(request.content, 'html.parser')
text = beau.get_text()

#setting the text to lowercase
text = text.lower()
#removing all the punctuation
text = text.translate(str.maketrans('', '', string.punctuation)) 
#separating the text into individual words
text = nltk.word_tokenize(text)
#making stopwords
stopWords = set(stopwords.words("english"))

#here we are removing words that did not appear in the article but showed up in the data frequency distribution 
removeWords = set(["accountsbest", "credit", "personal", "small", "loans"])
#creating a list of words while filtering out certain words such as the removeWords and stopWords 
text = list(word for word in text if word.isalpha() and len(word) > 2 and word.casefold() not in stopWords and word.casefold() not in removeWords)
#create a frequency distribution using text
frequencyPlot = FreqDist(text)

#creating a title for the plot, and setting it to font size 14
plt.title('Frequency Distribution 2022', fontsize = 14) 

#plotting 25 words
frequencyPlot.plot(25)

<Axes: title={'center': 'Frequency Distribution 2022'}, xlabel='Samples', ylabel='Counts'>


!pip install fredapi

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#the following two lines are required for macos only. They add the capacity to check #certificates and verify them. 
import ssl
ssl._create_default_https_context = ssl._create_unverified_context

from fredapi import Fred

#get the api key from fred website. 
fred = Fred(api_key='4df6af011a2d28b7e0e811da718ed7e0')  
#get the unemployment rate from the data for all years.
unemployment = fred.get_series('UNRATE', start_date='2018-01-01', end_date='2022-01-01')
#since the unemployment rates are not given for each day, we get all the data from 1900s to #end date. We get it on annual basis. 
print(unemployment.head)


import matplotlib.pyplot as plt
#plotting the unemployment rates for each year
plt.plot(unemployment)
plt.title('Unemployment Rate')
plt.xlabel('year')
plt.ylabel('Unemployment Rate')
#print the plot
plt.show()
#setting parameter of graph to increase graph size to analyze it better. 
plt.rcParams["figure.figsize"] = (30,15)

<bound method NDFrame.head of 1948-01-01    3.4
1948-02-01    3.8
1948-03-01    4.0
1948-04-01    3.9
1948-05-01    3.5
             ... 
2022-10-01    3.7
2022-11-01    3.6
2022-12-01    3.5
2023-01-01    3.4
2023-02-01    3.6
Length: 902, dtype: float64>

	AAPL							AMGN			...	WBA			WMT
	Adj Close	Average	Close	High	Low	Open	Volume	Adj Close	Average	Close	...	Low	Open	Volume	Adj Close	Average	Close	High	Low	Open	Volume
Date
2018-01-02	40.888062	42.695000	43.064999	43.075001	42.314999	42.540001	102223600	151.353928	176.120003	177.000000	...	72.970001	73.279999	6605000	89.607697	99.154999	98.589996	99.790001	98.519997	99.300003	10150500
2018-01-03	40.880951	43.313751	43.057499	43.637501	42.990002	43.132500	118071600	154.209991	178.599998	180.339996	...	74.309998	75.089996	7147300	90.389374	99.384998	99.449997	100.000000	98.769997	98.849998	8903600
2018-01-04	41.070839	43.193750	43.257500	43.367500	43.020000	43.134998	89738400	153.560104	179.805000	179.580002	...	70.730003	73.489998	12456000	90.471161	99.345001	99.540001	99.779999	98.910004	99.540001	6830400
2018-01-05	41.538441	43.552500	43.750000	43.842499	43.262501	43.360001	94640000	154.475052	179.955002	180.649994	...	71.820000	71.900002	9067200	91.007423	99.994999	100.129997	100.379997	99.610001	99.639999	7284000
2018-01-08	41.384155	43.692499	43.587502	43.902500	43.482498	43.587502	82271200	154.432327	179.839996	180.600006	...	72.750000	73.150002	5312500	92.352585	100.980000	101.610001	101.750000	100.209999	100.250000	8843900
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-12-23	131.658981	131.029999	131.860001	132.419998	129.639999	130.919998	63814900	261.612915	264.095001	263.920013	...	38.209999	38.560001	3476700	143.183151	143.040001	143.770004	143.800003	142.279999	143.050003	3182500
2022-12-27	129.831772	130.065002	130.029999	131.410004	128.720001	131.380005	69007800	261.087555	264.744995	263.390015	...	38.110001	38.619999	3940100	143.222977	143.820000	143.809998	144.449997	143.190002	144.039993	3348200
2022-12-28	125.847855	128.450001	126.040001	131.029999	125.870003	129.669998	85438400	259.134766	263.380005	261.420013	...	37.560001	38.250000	4008900	140.713257	142.290001	141.289993	143.669998	140.910004	143.470001	5082100
2022-12-29	129.412415	129.105000	129.610001	130.479996	127.730003	127.989998	75703700	260.859558	263.510010	263.160004	...	37.360001	37.660000	5251500	141.569748	142.385002	142.149994	143.020004	141.750000	142.139999	3057300
2022-12-30	129.731918	128.689999	129.929993	129.949997	127.430000	128.410004	77034200	260.344086	261.370010	262.640015	...	36.970001	37.240002	5406200	141.211227	141.400002	141.789993	141.990005	140.809998	141.559998	3835300

	Open	High	Low	Close	Adj Close	Volume
Date
2018-01-02	24809.349609	24864.189453	24741.699219	24824.009766	24824.009766	341130000
2018-01-03	24850.449219	24941.919922	24825.550781	24922.679688	24922.679688	456790000
2018-01-04	24964.859375	25105.960938	24963.269531	25075.130859	25075.130859	403280000
2018-01-05	25114.919922	25299.789062	25112.009766	25295.869141	25295.869141	358020000
2018-01-08	25308.400391	25311.990234	25235.410156	25283.000000	25283.000000	341390000
...	...	...	...	...	...	...
2018-12-21	22871.740234	23254.589844	22396.339844	22445.369141	22445.369141	900510000
2018-12-24	22317.279297	22339.869141	21792.199219	21792.199219	21792.199219	308420000
2018-12-26	21857.730469	22878.919922	21712.529297	22878.449219	22878.449219	433080000
2018-12-27	22629.060547	23138.890625	22267.419922	23138.820312	23138.820312	407940000
2018-12-28	23213.609375	23381.880859	22981.330078	23062.400391	23062.400391	336510000

	Open	High	Low	Close	Adj Close	Volume
Date
2020-01-02	28638.970703	28872.800781	28627.769531	28868.800781	28868.800781	251820000
2020-01-03	28553.330078	28716.310547	28500.359375	28634.880859	28634.880859	239590000
2020-01-06	28465.500000	28708.019531	28418.630859	28703.380859	28703.380859	252760000
2020-01-07	28639.179688	28685.500000	28565.279297	28583.679688	28583.679688	258900000
2020-01-08	28556.140625	28866.179688	28522.509766	28745.089844	28745.089844	291750000
...	...	...	...	...	...	...
2020-12-23	30046.730469	30292.529297	30046.730469	30129.830078	30129.830078	274050000
2020-12-24	30155.919922	30209.669922	30099.300781	30199.869141	30199.869141	145570000
2020-12-28	30283.230469	30525.560547	30283.230469	30403.970703	30403.970703	302490000
2020-12-29	30492.070312	30588.789062	30274.240234	30335.669922	30335.669922	357610000
2020-12-30	30415.089844	30525.349609	30393.039062	30409.560547	30409.560547	291890000

	Open	High	Low	Close	Adj Close	Volume
Date
2022-01-03	36321.589844	36595.820312	36246.449219	36585.058594	36585.058594	347930000
2022-01-04	36636.000000	36934.839844	36636.000000	36799.648438	36799.648438	435080000
2022-01-05	36722.601562	36952.648438	36400.390625	36407.109375	36407.109375	462040000
2022-01-06	36409.050781	36464.191406	36200.679688	36236.468750	36236.468750	385890000
2022-01-07	36249.589844	36382.839844	36111.531250	36231.660156	36231.660156	356110000
...	...	...	...	...	...	...
2022-12-23	32961.058594	33226.140625	32814.019531	33203.929688	33203.929688	221050000
2022-12-27	33224.230469	33387.718750	33069.578125	33241.558594	33241.558594	246010000
2022-12-28	33264.761719	33379.550781	32869.148438	32875.710938	32875.710938	252260000
2022-12-29	33021.429688	33293.421875	33020.351562	33220.800781	33220.800781	243060000
2022-12-30	33121.609375	33152.550781	32847.820312	33147.250000	33147.250000	295500000

	AAPL	AMGN	AXP	BA	CAT	CRM	CSCO	CVX	DIS	DOW	...	MRK	MSFT	NKE	PG	TRV	UNH	V	VZ	WBA	WMT
	High	High	High	High	High	High	High	High	High	High	...	Average	Average	Average	Average	Average	Average	Average	Average	Average	Average
Date
2018-01-02	43.075001	177.820007	99.730003	296.989990	159.389999	104.699997	38.950001	127.739998	111.809998	NaN	...	53.816793	85.904999	63.170000	91.240002	133.880005	221.224998	114.514999	53.184999	74.080002	99.154999
2018-01-03	43.637501	181.440002	99.760002	298.500000	157.490005	106.139999	39.279999	128.940002	113.190002	NaN	...	53.540075	86.240002	63.209999	90.804996	132.110001	222.165001	115.225002	52.760000	75.055000	99.384998
2018-01-04	43.367500	180.860001	101.650002	298.420013	159.580002	107.660004	39.540001	128.350006	113.000000	NaN	...	54.293894	87.115002	63.049999	91.189999	133.055000	225.135002	116.695000	52.360001	72.115002	99.345001
2018-01-05	43.842499	180.880005	101.080002	308.890015	162.050003	108.300003	39.880001	128.100006	112.680000	NaN	...	54.379772	87.920002	63.885002	90.935001	131.974998	226.885002	117.709999	52.615000	72.535000	99.994999
2018-01-08	43.902500	181.250000	101.199997	310.859985	166.429993	109.139999	39.959999	128.630005	111.279999	NaN	...	54.270039	88.090000	64.344999	91.349998	131.750000	226.660004	119.275002	52.395000	73.375000	100.980000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-12-23	132.419998	265.290009	147.139999	189.429993	240.539993	129.860001	47.490002	177.580002	88.070000	50.910999	...	111.500000	236.404999	116.474998	152.294998	188.389999	527.105011	204.880005	38.230000	38.484999	143.040001
2022-12-27	131.410004	266.609985	147.860001	192.440002	245.050003	131.750000	47.709999	180.229996	87.940002	51.220001	...	112.059998	237.379997	117.009998	153.490005	190.059998	532.845001	205.740005	38.860001	38.395000	143.820000
2022-12-28	131.029999	265.619995	146.820007	191.320007	243.820007	131.139999	47.770000	179.300003	86.690002	51.349998	...	111.974998	236.945000	116.599998	153.274994	189.665001	532.940002	206.665001	39.125000	38.065001	142.290001
2022-12-29	130.479996	264.880005	147.619995	190.250000	241.610001	132.949997	47.740002	179.199997	88.239998	50.801998	...	111.224998	238.784996	116.935001	152.690002	189.000000	531.269989	207.014999	39.170000	37.665001	142.385002
2022-12-30	129.949997	263.230011	147.929993	190.649994	240.160004	132.639999	47.669998	179.949997	87.120003	50.530998	...	110.770000	238.310005	116.509998	151.559998	187.915001	527.670013	206.869995	39.379999	37.175001	141.400002

STA 141B Final Project¶

Project Outline¶

Part 1: Introdcution¶

Part 2: Graphs and Visualizations¶

Part 3: NLP Analysis for Stock Market news from CNBC¶

Part 4: API key for Unemployment rates throughout Covid-19 Pandemic¶

Part 6: Conclusion¶

References¶